Detection of Wagyu beef sources with image classification using convolutional neural network

Nattakorn Kointarangkul; Yachai Limpiyakorn

doi:10.1117/12.2604971

6 August 2021 Detection of Wagyu beef sources with image classification using convolutional neural network

Nattakorn Kointarangkul, Yachai Limpiyakorn

Proceedings Volume 11913, Sixth International Workshop on Pattern Recognition; 119130J (2021) https://doi.org/10.1117/12.2604971
Event: Sixth International Workshop on Pattern Recognition, 2021, Chengdu, China

Abstract

Wagyu beef originated in Japan. There are many types of Wagyu beef in the market around the globe, though. Primary sources may include Australia, the United States of America, Canada, and the United Kingdom. The authentic Japanese Wagyu is well known for its intense marbling, juicy rich flavor, and tenderness. And there are differences in flavor, texture, and quality between the different types of Wagyu. Nowadays, there is a growing interest in deep learning as a remarkable solution for several domain problems such as computer vision and image classification. In this study, we thus present an AI-based approach to identifying Wagyu beef sources with image classification. A deep neural network, CNN, was constructed to detect the marbled fat patterns of two sources, Japanese Wagyu and Australian Wagyu. The images were collected from reliable sources on the internet and augmented with DCGAN. The prediction of Wagyu sources achieved high accuracy of 95%. The learning model of Convolutional Neural Networks was found to be a promising method for the rapid characterization of the unique patterns of marbled fat layers. The classifier would benefit the customers for buying what they expect from the products in terms of quality and taste.

Citation Download Citation

Nattakorn Kointarangkul and Yachai Limpiyakorn "Detection of Wagyu beef sources with image classification using convolutional neural network", Proc. SPIE 11913, Sixth International Workshop on Pattern Recognition, 119130J (6 August 2021); https://doi.org/10.1117/12.2604971

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available